Reginaldo Barbosa Nunes
@ifes.edu.br
Electrical Engineering
Federal Institute of Espirito Santo
Graduated in Electrical Engineering from the Federal University of Espírito Santo (UFES) (1988), Masters in Computer UFES (2003), and PhD. in Electrical Engineering from UFES (2016). Professor of higher and technical education at the Federal Institute of Espírito Santo (Ifes). Has experience in Computer Science and Telecommunications area. More specifically Computer Networks, Telecommunication Systems, Wireless Networks, Passive Optical Networks, Access Networks, and OFDM systems.
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering, Computer Networks and Communications, Signal Processing, Control and Optimization
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Márcio Có, Flávia Consoni, Matheus Coelho Carneiro, Guilherme Fernandes, Reginaldo Barbosa Nunes, and Clainer Donadel
MDPI AG
In the transition to electric mobility (EM), business model innovation plays a crucial role in expanding the use of electric vehicles and increasing acceptance of this technology. The transition pathways differ between countries in the largest economies and those in Latin America. Brazil presents a unique scenario, benefiting from an early start with biofuels, the production of flex-fuel vehicles, predominantly renewable electric energy generation (>80%), and the absence of a structured national policy aligned with local governments. This study introduces a framework that surveys and categorizes businesses in EM, relating them to sustainable development aspects and regulatory maturity. It builds a solid conceptual foundation, incorporating data from technical and commercial events, as well as interviews with Brazilian specialists for validation. The proposed framework aids in understanding the Brazilian context, identifying regulatory gaps, and developing a common language to advance studies on business model innovation, contributing to electromobility studies in Latin America. Additionally, it can guide the construction of regional and local public policies and help identify more sustainable projects.
Andriele Ninke, João Thomaz Lemos, Pablo Rodrigues Muniz, Reginaldo Barbosa Nunes, Hércules Lázaro Morais Campos, and Josemar Simão
Springer Nature Switzerland
Raphael Cruz Alves, Rodrigo Varejão Andreão, and Reginaldo Barbosa Nunes
IEEE
The rapid adoption of electric vehicles (EVs) necessitates advanced and efficient charging management systems. This paper introduces an AI-based charging system that emphasizes the importance of load management, equitable energy distribution, and the detection of charging anomalies. Designed to provide users with accurate cost forecasts and proactive anomaly detection, our proposed system aims to enhance the EV charging experience. Implemented on the Azure cloud platform, the system's core functionalities have been proven effective. Through a case study conducted in Espírito Santo, Brazil, we demonstrate the capability of our system to improve the EV charging infrastructure. While the potential for integration with smart grid data exists, the primary focus of this work is on the standalone capabilities of the charging system, laying the groundwork for future smart grid integrations.
Renner Sartório Camargo, Vinícius Secchin De Melo, Marcelo Brunoro, Weder Tótola Nunes, Filipe Krebel, Monique Cardoso Fernandes, and Reginaldo Barbosa Nunes
IEEE
The Brazilian power grid is predominantly composed of renewable sources such as hydroelectric, wind, solar, and biomass. However, the electrification of vehicles, including electric and hybrid vehicles, is a global trend that could potentially strain the Brazilian power system if not properly planned, compromising decarbonization efforts. One solution could be the adoption of the Vehicle-to-Grid (V2G) philosophy, which involves supplying electricity from the batteries of connected electric vehicles (EVs) back to the grid to meet part of the peak demand. This implementation could save costs on non-renewable fuel-powered thermoelectric plants and reduce the EV carbon footprint, contributing to a more resilient power system. The focus of this article is to analyze how the charging and discharging of vehicles in a V2G system impact the demand of the Southeast/Midwest (Sudeste/Centro-Oeste - SE/CO) subsystem, the largest among those that compose the National Integrated System (Sistema Integrado Nacional- SIN), considering various scenarios and their impact on the characteristic load curve.
Filipe Krebel, Weder Tótola Nunes, Vinícius Secchin De Melo, Renner Sartório Camargo, Monique Cardoso Fernandes, Marcelo Brunoro, and Reginaldo Barbosa Nunes
IEEE
Climate change and the future scarcity of fossil fuels are driving the actions of the so-called energy transition. The transport sector is at the center of this problem and the introduction of electric vehicles is appearing as the main solution for this sector. However, the possibility of a mass insertion is surrounded by doubts about the impacts on energy demand. Some works analyzed the Brazilian scenario considering different EV insertion rates in a short time interval. Unlike what is found in similar works, this study tries to approach a scenario that takes into account the socioeconomic aspects of Brazil to analyze the impacts that the introduction of the electric fleet will bring to the national electric matrix. The results indicate that this insertion will be slow and gradual, with negligible impacts in the first decades.
Victor Manuel Riva De Oliveira, Vinícius Ferreira dos Remédios, Clainer Bravin Donadel, Walbermark Marques Dos Santos, Márcio Almeida Có, Vinícius Secchin de Melo, and Reginaldo Barbosa Nunes
IEEE
The increasing concern about climate change has been leading researches in many areas to investigate solutions for this issue. An emerging subject related to it is electric vehicles (EV). Although it is an important topic, there is still a lack of review papers about EVs modeling. In this context, this paper aims to bring a review about EV modeling to fulfill this gap. A wide bibliography review is made to cover different kinds of EV s modeling, in many aspects. As a conclusion, in some areas, authors do not diverge from each other, while in others may exist a large range of different ways to model the EV s. In this case, researchers should choose the modeling that fits the best for their research, which may vary in simplicity and computational effort.
Kemily Monteiro Cardoso Goularte, Pablo Rodrigues Muniz, and Reginaldo Barbosa Nunes
IEEE
The Electric Energy Social Tariff (TSEE) is a social benefit created by the Federal Government of Brazil in 2002 to benefit energy consumers from the Low-Income Residential Subclass by granting discounts on the electricity bill. In an audit carried out by the Federal Court of Auditors (TCU - Tribunal de Contas da União - in Portuguese), it was found that the criterion for including families in the subsidy - monthly electricity consumption - is ineffective, since it has a low relationship with family income. Another audit carried out by the TCU discovered the lack of strategic design and key indicators for this social subsidy. Given this, this paper characterizes the per capita income profile and discounts granted to these families, focusing on the Brazilian State of Espírito Santo. The impacts of two law projects that are being discussed in the Brazilian Senate were also analyzed. The results indicate that families with higher per capita income predominantly receive more significant discounts on their electricity bills, confirming the audits' hypothesis. It was also found that the law projects improve the situation a little, but maintain this inconsistency and increase the financial value of the subsidy granted.
Vinícius S. De Melo, Renner S. Camargo, Filipe Krebel, Marcelo Brunoro, Weder T. Nunes, Monique C. Fernandes, and Reginaldo B. Nunes
IEEE
With the growing rate of greenhouse gas emissions in the transport sector, research and development of new technologies for producing less polluting and more efficient combustion engines have been constant. However, the reductions have not been so significant, as the vast majority of these engines are still dependent on fossil fuels. Electric Vehicles are one of the main alternatives for reducing greenhouse gases. However, its high cost and charging infrastructure are still barriers in the current scenario, but, in the long term will become highly competitive and tend to dominate the market in the transport area. In this work, projections of the light vehicle fleet evolution in Brazil will be presented, until 2050, considering the penetration of the main types of electric vehicles. Based on government data from the expansion of the interconnected electrical system, estimates of indirect emissions from recharging electric vehicle batteries are presented for low and high-electric mobility scenarios. Estimates show a significant reduction in CO2 emissions, even considering the low mobility scenario, where part of the combustion fleet is still present. A particularity is observed about plug-in hybrid vehicles, as their autonomy in electric mode is sufficient for daily driving demands, and emissions may suffer more significant reductions.
Pablo Rodrigues Muniz, Josemar Simão, Reginaldo Barbosa Nunes, Hércules Lázaro Morais Campos, Natália Queirós Santos, Andriele Ninke, and João Thomaz Lemos
Elsevier BV
Joao Pedro Da Silva Rodrigues, Weder Totola Nunes, Marcelo Brunoro, and Reginaldo Barbosa Nunes
IEEE
The amount of energy required by the transport sector is largely based on fossil fuels, but it is a finite source of energy. The use of these fuels results in the emission of pollutants such as greenhouse gas. As an alternative to reducing pollutants in the transport sector, there is the alternative of replacing the fleet with electric vehicles. However, if the electrical matrix depends on non-renewable energy sources, the objective of reducing pollutants may not be achieved, in addition to interfering with the efficiency of energy use. As a way of evaluating the energy efficiency of different vehicular propulsion, a well-to-wheel (WTW) efficiency analysis was performed considering vehicles with internal combustion engine (ICE) based on gasoline, compressed natural gas, diesel and ethanol. For electric vehicles, hybrid electric vehicles (HEV) operating with gasoline and diesel, and battery electric vehicles (BEV) were considered. As an evaluation tool, calculations with WTW efficiency values were performed. The values referring to the weel-to-tank (WTT) and tank-to-wheels (TTW) stages were obtained from the literature. For the production of electricity, non-renewable sources of energy (natural gas, diesel and nuclear generators) and renewable sources (hydroelectric, solar and wind) were considered. The results obtained indicate that among the ICE category, diesel-powered ICE is the category that has the highest WTW efficiency. In addition, the lowest efficiency of ICE is represented by vehicles powered by ethanol. For electric vehicles, it was observed that the best WTW general efficiency values are obtained when renewable energy sources are used as electrical energy supply to the BEV.
Joabe R. da Silva, Gustavo M. de Almeida, Marco Antonio de S. L. Cuadros, Hércules L. M. Campos, Reginaldo B. Nunes, Josemar Simão, and Pablo R. Muniz
MDPI AG
The COVID-19 pandemic has detrimentally affected people’s lives and the economies of many countries, causing disruption in the health, education, transport, and other sectors. Several countries have implemented sanitary barriers at airports, bus and train stations, company gates, and other shared spaces to detect patients with viral symptoms in an effort to contain the spread of the disease. As fever is one of the most recurrent disease symptoms, the demand for devices that measure skin (body surface) temperature has increased. The thermal imaging camera, also known as a thermal imager, is one such device used to measure temperature. It employs a technology known as infrared thermography and is a noninvasive, fast, and objective tool. This study employed machine learning transfer using You Only Look Once (YOLO) to detect the hottest temperatures in the regions of interest (ROIs) of the human face in thermographic images, allowing the identification of a febrile state in humans. The algorithms detect areas of interest in the thermographic images, such as the eyes, forehead, and ears, before analyzing the temperatures in these regions. The developed software achieved excellent performance in detecting the established areas of interest, adequately indicating the maximum temperature within each region of interest, and correctly choosing the maximum temperature among them.
Reginaldo B. Nunes, Diogo V. N. Coelho, Gianni L. S. Oliveira, Helder R. O. Rocha, Jair A. L. Silva, and Marcelo E. V. Segatto
Optica Publishing Group
The feasibility of a time-domain technique, proposed for uplink synchronization of the bandwidth scalable orthogonal frequency division multiple access passive optical network (BS-OFDMA PON), is experimentally demonstrated. The results show that a delay variation of less than 50% of the cyclic prefix is tolerable, relaxing the thin time synchronization required in the uplink of the evaluated architecture. Moreover, we enhance the spectral efficiency of the BS-OFDMA PON by reducing the guard band ( B g ) between the optical carrier and the signal bandwidth, as well as by eliminating the frequency gap ( Δ f G ) between the optical network units. The downlink results demonstrate that Δ f G can be eliminated with negligible performance penalties, and B g can be reduced to almost half the signal bandwidth. Error-free transmissions were achieved after uplink propagation over 45 km of single-mode fibers with received power of ≈ − 16.8 d B m and B g = 450 M H z , with a signal bandwidth of 1 GHz.
Gustavo A. Affonso, Alvaro L.L. De Menezes, Reginaldo B. Nunes, and Douglas Almonfrey
IEEE
Identifying suspicious activity in public areas is a common concern in machine learning studies. However, this task is not usually trivial. In this article, we propose a method for recognizing anomalies in the public transportation environment to guarantee the safety of its passengers. The method in question consists of a classifier based on Convolutional Neural Networks (CNN). As input to this model, we use images from Closed-Circuit Television (CCTV) cameras already present in vehicles for security reasons. The addressed problem became challenging for reasons such as the lack of standardization of equipment, the low quality of the images provided, and the poor positioning of the cameras. In addition, a dataset, which has a high imbalance between the classes, was built. We evaluated four CNN architectures on the dataset to validate the proposed method. Experiments on the created dataset showed that the proposal of this project achieved promising results.
Alvaro L. L. de Menezes, Rafael C. de Almeida, Douglas Almonfrey, and Reginaldo B. Nunes
IEEE
Controlling vehicle occupancy is very useful for detecting overcrowding situations in public transportation. In a pandemic, it becomes essential to prevent disease spread by dynamically adapting the route plan and traffic scheduling and allowing passengers to choose vehicles that carry fewer passengers in real-time. However, given the size and complexity of the system, this task is not trivial. In this paper, a classification method for passenger occupancy analysis is proposed. The classifier is based on a Convolutional Neural Network (CNN) model. As input to the proposed model, images from Closed-Circuit Television (CCTV) cameras, already present in the vehicles for security reasons, were used. The addressed problem is especially challenging due to the non-standardization of the equipment, poor quality of the images, and cameras’ variable positioning inside the vehicle. Besides, as there is no pre-existing image dataset for training, one must deal with a small number of labeled images. Two datasets are created, varying on the assessed quality score. Three CNN architectures were evaluated on the datasets, to validate the feasibility of the proposed method. Experiments on the created public transportation dataset show that the proposal of this work achieves promising results.
Felipe de Souza Santos, Yngrith Soares Da Silva, Joabe Ruella Da Silva, Josemar Simao, Hercules Lazaro Morais Campos, Reginaldo Barbosa Nunes, and Pablo Rodrigues Muniz
IEEE
Due to the Covid-19 pandemic, many places with high traffic of people set up sanitary barriers to screening febrile people. Thermal imagers and pyrometers are the equipment commonly used to measure temperature in these barriers. In this paper, Temperature measurements performed by these instruments were compared considering the regions usually monitored in these barriers: forehead, ear, and wrist. The temperatures of volunteers were recorded after initial acclimatization, a short period of physical exercise, and a rest period after exercise, evaluating the instruments and regions of measurement and the influence of the condition of rest and physical exercise of the person. The compatibility of the performed measurements in each of these three moments was then compared using statistical and metrological tools. The outcomes showed that the pyrometer has low repeatability since its results vary widely among different measurements due to the temperature variation existing within the analyzed region and the difficulty of guaranteeing the same measurement point. As a result, a thermal imager is more recommended for taking body temperature measurements on the forehead and, preferably, on the ear. It was also determined that the wrist, in particular, has a significantly lower temperature than the central body temperature, and thus this is not a recommended region to measure body temperature when screening febrile people.
Joabe Ruella da Silva, Yngrith Soares da Silva, Felipe de Souza Santos, Natalia Queiros Santos, Gustavo Maia de Almeida, Josemar Simao, Reginaldo Barbosa Nunes, Marco Antonio de Souza Leite Cuadros, Hercules Lazaro Morais Campos, and Pablo Rodrigues Muniz
IEEE
A pandemia do COVID-19 tem afetado a vida das pessoas bem como as economias de vários países, os setores de saúde, educação, transporte, entre outros. Para tentar conter a disseminação do vírus, diversos países implementaram barreiras sanitárias em aeroportos, rodoviárias, estações, portarias das empresas e outros espaços compartilhados para detectar pacientes com algum sintoma da infecção viral. Visto que a febre é um dos sintomas mais recorrentes da doença, iniciou-se uma corrida aos mercados por dispositivos de medição de temperatura corporal. As câmeras termográficas, também conhecidas como termovisores, são outros dispositivos utiliza-dos para medir a temperatura, empregando tecnologia conhe-cida como termografia infravermelha, uma ferramenta não invasiva, rápida e objetiva. Neste estudo aplicouse a transferência de aprendizado de máquina na YOLO para detectar as regiões mais quentes da face humana em imagens termográficas, permitindo a identificação de estado febril em humanos. Para isso, os algoritmos de inteligência artificial detectam as regiões de interesse nas imagens termográficas, que são: os olhos, a testa e os ouvidos e em seguida, são analisadas as temperaturas nestas regiões. O software desenvolvido apresentou excelente desempenho na detecção das regiões de interesse estabelecidas, que indica adequadamente a máxima tempera-tura dentro das regiões de interesse, e que a escolha do método de máxima temperatura se apresentou adequada.
Karina Andreatta, Filipe Apostolo, and Reginaldo Nunes
IEEE
This paper describes the design and implementation of a soft sensor based on backpropagation neural network model to predict the cement fineness online in a ball mill. The input variables of these models were selected by studying the cement grinding process. The fineness results of laboratory tests were collected to obtain the output variable. This paper introduces a procedure for the extraction, analysis, treatment, and cleaning of raw data received from the factory, which provided a low prediction error. Estimating this variable in real-time can be extremely useful for maintaining the desired fineness during the cement grinding process, which will also allow a significant increase in the system energy efficiency. The model that presented the highest performance and the ability to predict fineness was selected to implement as a soft sensor. The developed system was tested in a cement mill grinding process, and good results were achieved, demonstrating the ability to provide information about the variables previously obtained only through offline laboratory tests.
Helder Roberto de O. Rocha, Vinicius O. C. Dias, Esequiel da Veiga Pereira, Reginaldo Barbosa Nunes, Marcelo E. V. Segatto, and Jair A. L. Silva
Institute of Electrical and Electronics Engineers (IEEE)
A multi-objective optimization was implemented to increase the spectral efficiency of direct-detection optical orthogonal frequency division multiplexing (OFDM) systems with constant-envelope (CE) signals. Based on a genetic algorithm, the optimization procedure evaluates the impact of fiber injection power in the reduction of the guard band between the optical carrier and the CE-OFDM signals, optimizing important parameters such as electrical phase modulation and optical modulation indices of the denominated DDO-CE-OFDM system. Simulation results show that a guard-band reduction around <inline-formula><tex-math notation="LaTeX">$40\\%$</tex-math></inline-formula> was achieved in a 4 Gbps optimized DDO-CE-OFDM system with 16-QAM subcarrier mapping and <inline-formula><tex-math notation="LaTeX">$-7$</tex-math></inline-formula> dBm fiber input power. Results obtained in an experimental proof-of-concept conducted to validate the optimization procedure show that this reduction can reach <inline-formula><tex-math notation="LaTeX">$66\\%$</tex-math></inline-formula> according to a power penalty of <inline-formula><tex-math notation="LaTeX">$\\approx 2$</tex-math></inline-formula> dB demanded by the inherent spectral broadening of CE-OFDM signals, after propagation over 40 km of standard single-mode fiber.
Caio M. Santos¹, R. B. Nunes², J. A. L. Silva¹, M. E. V. Segatto¹, and M. J. Pontes¹
OSA
We discuss the use of an optical recirculating loop for the experimental transmission of DDO-OFDM signals. Although the bitrate is modest, transmission distances as long as 900 km were obtained using direct detection.
Reginaldo B. Nunes, João M.R. Bacalhau, Jair A.L. Silva, and Marcelo E.V. Segatto
Elsevier BV
Carlos A. Dalarmelina, Saheed A. Adegbite, Esequiel da V. Pereira, Reginaldo B. Nunes, Helder R. O. Rocha, Marcelo E. V. Segatto, and Jair A. L. Silva
SPIE-Intl Soc Optical Eng
Abstract. Block-level detection is required to decode what may be classified as selective control information (SCI) such as control format indicator in 4G-long-term evolution systems. Using optical orthogonal frequency division multiplexing over radio-over-fiber (RoF) links, we report the experimental evaluation of an SCI detection scheme based on a time-domain correlation (TDC) technique in comparison with the conventional maximum likelihood (ML) approach. When compared with the ML method, it is shown that the TDC method improves detection performance over both 20 and 40 km of standard single mode fiber (SSMF) links. We also report a performance analysis of the TDC scheme in noisy visible light communication channel models after propagation through 40 km of SSMF. Experimental and simulation results confirm that the TDC method is attractive for practical orthogonal frequency division multiplexing-based RoF and fiber-wireless systems. Unlike the ML method, another key benefit of the TDC is that it requires no channel estimation.
Reginaldo B. Nunes, Ali Shahpari, Jair A. L. Silva, Mario Lima, Paulo S. B. de Andre, and Marcelo E. V. Segatto
Institute of Electrical and Electronics Engineers (IEEE)
Spectral efficiency is one of the critical issues in passive optical networks (PONs). Thus, next generation PON systems must employ advanced modulation techniques and channel compensation to improve the network performance and provide more bandwidth for end users. In this letter, we have experimentally demonstrated a spectral efficiency improvement in PONs based on the bandwidth scalable orthogonal frequency division multiplexing (BSOFDM-PON), considerably reducing the recommended optical guard band of such direct detection systems. The experimental results show that this efficiency can be achieved in a 33.5-Gb/s BSOFDM-PON on one wavelength channel when subcarrier pre-emphasis is used.
Esequiel V. Pereira, Helder R.O. Rocha, Reginaldo B. Nunes, Marcelo E. V. Segatto, and Jair A. L. Silva
IEEE
A Multi-objective optimization on the parameters of direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems in short-range links is proposed. Based on Genetic Algorithms (GA), the optimization process takes into account the influence of optical power in the guard-band reduction of such multicarrier systems with 4 and 16-QAM subcarrier mapping and for propagation in 40 km of standard single-mode fiber (SSMF). Simulation results show that the parameters optimization is responsible for a gain of ≈ 2.5 dB [≈ 1.8 dB] in the required optical power for a bit-error-rate of 10-3, for DDO-OFDM system transmission over 40 km of uncompensated SSMF, with a guard band (BG) equals to 50% of the signal bandwidth (Bw) and 16-QAM [4-QAM] subcarrier mapping. For a extremely reduced guard-band of only 1% of Bw, the gain reduced to ≈ 0.5 dB and ≈ 1.5 dB for 16-QAM and 4-QAM mapping, respectively.
Esequiel da V. Pereira, Helder R. de O. Rocha, Reginaldo B. Nunes, Marcelo E. V. Segatto, and Jair A. L. Silva
Institute of Electrical and Electronics Engineers (IEEE)
A multiobjective optimization on the parameters of direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems in short-range links is proposed. Based on genetic algorithms, the optimization process takes into account the influence of optical power in the guard-band reduction of such multicarrier systems with 4and 16-quadratic-amplitude modulation (QAM) subcarrier mapping and for propagation in 40 km of standard single-mode fiber (SSMF). Simulation results show that the parameters optimization is responsible for a gain of approximately 2.5 dB [≈ 1.8 dB] in the required optical power for bit-errorrate ≤10-3, for DDO-OFDM system transmission over 40 km of uncompensated SSMF, with a guard band BG equals to 50% of the signal bandwidth Bw and 16-QAM [4-QAM] subcarrier mapping. For an extremely reduced guard band of only 1% of Bw, the gain reduced to ≈ 0.5 dB and ≈ 1.5 dB for 16and 4-QAM mapping, respectively. The optimization process is experimentally validated under the transmission of the optimized DDO-OFDM systems in optical back-to-back (B2B) configuration and 40 km of uncompensated SSMF. A fair performance comparison is discussed in the experimental results considering BG/Bw = 0.5 and BG/Bw = 0.01 in the range of the optimized optical powers. It is shown that a maximum optical power penalty of approximately 2 dBm is registered according to a guard-band reduction from 50% to 1% of the useful bandwidth, for transmission over the 40 km of SSMF, considering 16-QAM subcarrier mapping.
Sergio Juliao, Reginaldo B. Nunes, Diogo Viana, Paulo Jesus, Nelson Silva, Arnaldo S. R. Oliveira, and Paulo Monteiro
IEEE
We propose a flexible optical SCM modem to transmit, with high spectral efficiency, mobile front-haul signals. The real time experimental results, demonstrate the multicarrier aggregation capacity of 3.6 Gbps based on four 1024-QAM D-RoF subcarriers and a 30km transmission reach.